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ai-adoption
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Heuristic AI literacy is not a training problem Treat AI literacy as a durable mental-model shift, not an event — the judgement required to use AI well cannot be installed through a workshop. Heuristic AI onboarding teaches both the person and the AI A first encounter between a staff member and an organisation's AI has two students; a useful onboarding step changes the user's understanding and writes to the AI's persistent context, and steps that achieve only one are filler. Heuristic Make the firm itself a Claude project A shared Claude project loaded with the firm's policies, values, governance and knowledge sources, queryable as the first-line answer to internal questions, has emerged as a reproducible deployment pattern across engagements. Heuristic Embed the AI policy in the AI itself Load the AI policy into the tool's system instructions and direct staff to ask the tool about its own rules; the policy delivers itself. Heuristic Match AI programme ambition to working-team capability AI programme designs that look right on paper depend on the working team's technical confidence to execute; calibrate the design to the team you have, not the team the design assumes. Heuristic Measure adoption, not just implementation Deploying an AI tool and reporting success are not the same thing; track active use rather than availability, because the gap between the two is where unvoiced resistance hides and where the investment fails to earn its return. Heuristic Policy approval is the start, not the end Treat AI policy approval as the start of governance work, not the end — operating procedures, impact assessments, tool-specific guidelines, training, and the approved-tools register all ramp up after the policy is signed. Heuristic Structure documents for AI consumption, not just human reading Human-formatted documents obstruct AI consumption; plain-text formats such as Markdown let AI work with the underlying knowledge efficiently. Heuristic Useful AI is a context problem The difference between useful AI and dangerous AI is almost entirely about the context it has; output quality is bounded above by input quality. Pattern AI as an operational interpreter of purpose, vision and values AI may offer a different mechanism for translating stated purpose, vision and values into daily operational decisions — continuous rather than episodic, contextual rather than general, and individually available rather than programme-delivered. Whether the mechanism proves durable in practice is an open question. Pattern AI treats documentation as authoritative In the pre-AI world, messy or incomplete documentation was tolerable because humans interpreted around it; AI does not, and instead surfaces and propagates errors, stale content, and inconsistent processes — which changes the maintenance burden of every document an AI can see. Case study An 'Ask the Org' knowledge-base rollout in a mid-sized organisation A mid-sized national organisation deploys an "Ask the Org" Claude project as a retrieval layer over its existing knowledge stack rather than migrating platforms; the architecture and pilot decisions, and the cluster of principles they instantiate. Heuristic Treat AI-pilot bypass behaviour as evaluation data When pilot users route around the AI to access the underlying source directly, treat that as informative signal about deployment quality — not as evidence the AI isn't useful. Heuristic Channel shadow AI use as signal, not risk to suppress In most organisations, staff are already using AI in ways leadership has not sanctioned; treating that shadow use as evidence of real work-in-context rather than as compliance risk reveals use cases, knowledge gaps and adoption blockers that top-down planning will not find. Heuristic A document store is not a knowledge management system Shelving documents in a repository is storage, not knowledge management; the presence of the repository often produces false confidence that the problem is solved. Heuristic Expect current AI deployments to look primitive in retrospect Current AI deployments mostly fit the technology into existing workflows; treat today's designs as transitional and expect later shapes to differ fundamentally. Heuristic Involve sceptics early in AI initiatives Sceptics are more valuable than advocates during the design of an AI initiative — they see the failures most clearly; involve them early in roles that protect against the failures they fear, rather than sidelining them as resistant to change. Heuristic Make tacit knowledge explicit, or AI cannot use it AI cannot interpret the unwritten assumptions that shape how an organisation actually works; operational self-description is precondition, not polish. Heuristic Passive AI adoption is an implicit policy choice Where an organisation has not made explicit decisions about how AI will be used, the defaults of the tools and vendors become policy by inheritance; "we haven't decided yet" functions as "we have accepted whatever happens". Heuristic Sequence transformation programmes; do not run them in parallel Mid-tier firms running multiple concurrent transformation programmes hit a coordination ceiling that makes any single programme stall; complete critical migrations before adding AI complexity. Heuristic Frame content-and-data programmes by change-cost tier When proposing a content or data architecture programme, separate it into a backend-only tier (no ask of staff), a modest-asks tier (light behavioural change), and a full-reset tier (enforced behaviour change) — the tiers expose the trade-off and most engagements settle in the middle. Case study A tools-first AI rollout that plateaued An abstracted composite showing what happens when a mid-tier firm buys AI tools without putting its information in order first. Pattern AI's first organisational gain is transparency, not automation The dominant industry focus on agentic automation is misallocated for most organisations; the more significant near-term gain is making documented organisational knowledge reliably accessible through AI — automation comes later, and depends on the transparency arriving first. Pattern The mid-tier AI adoption threshold In mid-tier organisations, the daily pressure of business-as-usual sets a payoff threshold that typical AI gains do not clear, so adoption stalls even when tools and training are in place. Case study A mid-tier firm's pivot from AI committee to delivery cell An abstracted single-engagement case study showing how a mid-tier professional services firm shifted from a deliberative AI committee to a small delivery cell after the committee structure failed to move the dial. Pattern AI's most dangerous failure mode is confident wrongness AI's most dangerous failure is not silence but fluent, authoritative output that is wrong — making error detection a skilled, human task that cannot be deferred to the tool. Heuristic Start with knowledge management, not tools Audit and structure what the organisation knows before selecting AI tools; the limits of AI output are set by the limits of its input context. Heuristic Use a frontier LLM as a personal AI mentor Use a frontier LLM as a conversational partner for learning about AI itself — ask it about its capabilities, limitations and appropriate use cases while doing real work with it. The self-directed, contextualised learning this produces outperforms the structured training programmes it replaces. Heuristic Prototype to specify, not to deliver When prototypes are cheap, they substitute for specifications and surface requirements ambiguities specifications would not. Heuristic Separate deliberation from delivery in AI initiatives Committees deliberate well and deliver poorly; once delivery is the bottleneck, separate the functions and move delivery to a small dedicated cell. Pattern Unvoiced staff resistance is the primary failure mode of AI initiatives The most insidious threat to AI adoption is not technical or budgetary but behavioural — staff publicly support the initiative while privately declining to adopt it, expressing resistance through plausible non-compliance rather than open challenge. Pattern AI as a labour service bypasses the adoption problem A delivery model in which vendors sell finished work product, not AI tools, removes internal-adoption friction from the buyer side and accelerates displacement timelines. Heuristic Expect AI to surface authenticity gaps between stated and actual values An AI system that takes an organisation's stated values seriously will quickly surface where stated and actual behaviour diverge; leadership should expect and plan for these findings before commissioning the work, because surfacing them without being prepared to respond is worse than not surfacing them at all. Pattern Context rot As AI-generated content feeds back into the organisation's context — documents, transcripts, summaries — today's hallucinations become tomorrow's training data, and the quality of the context degrades over time unless the cycle is actively broken. Pattern The first reader is an AI A growing share of inbound material at mid-tier firms is first read by an AI before a human sees it; the human who engages does so through the AI's rendering, changing what the deliverable has to carry and how the sending firm should produce it. Pattern AI interfaces are generated on demand rather than fixed by design The user interface layer, built historically as fixed buttons and menus that bridge human intent and machine execution, is being replaced piecemeal by AI-generated surfaces built at runtime in response to specific requests; wrappers that sit between user and base model are increasingly a liability rather than an aid. Case study An ongoing AI advisory engagement with a growing firm An abstracted single-engagement case study showing how a growing firm used an ongoing AI-strategy advisory relationship — covering market scanning, implementation oversight and staff coaching — to navigate AI adoption without diverting internal attention from operational delivery. Heuristic Polish and volume no longer signal effort The signals that used to tell reviewers about work quality — volume, polish, comprehensiveness — correlated with effort because effort was scarce; with AI the correlation breaks, and the questions that still discriminate are about process. Case study A regional bank's core banking selection delivered by an AI-amplified solo engagement An abstracted single-engagement case study showing how a solo Shepherd Thomas consultant, AI-amplified, delivered a regional bank's core banking system selection on a compressed timeline, at lower cost and comparable quality to a major consulting team. Heuristic Sort clients by AI posture and serve both groups deliberately Client bases are splitting along AI-forward, moving-slowly and AI-averse lines; firms that run a single operating mode for everyone will produce the wrong shape of work for a growing share of their book, and need to classify and serve the segments deliberately.